Most of the successful systems for sentiment classification present in the art are custom developed for the domain which involves huge cost in building and maintenance. They use the conventional features like lexical, semantic and patterns. Huge lookup databases are built to boost the accuracy of the tools. Heavy analysis on the grammar which is used in the training set which represents specific category of documents is provided as intelligence in the tool. The tools accuracy heavily depends on the training data as they represent the entire text for which the tool is built. Hence, most of these systems are not easily configurable or extendable. They involve heavy maintenance from time to time. Hence, such systems are very expensive for the companies to put to use.
Machine learning models provide easy maintenance but the training data need to be huge to represent the domain. In-depth technical knowledge is required to understand the behavior of such models and reason the results or mistakes made in prediction of the polarity scores. Therefore, Machine learning models are still not widely to predict polarity scores. So, there is a need in the art to build a solution, for slang sentiment classification for opinion mining which also calculates the polarity score.